# asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamics

arXiv: https://arxiv.org/abs/2604.24916 · HTML: https://mlsanity.com/paper/2604.24916/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 9 of 9)

- [BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation](https://mlsanity.com/paper/2403.09227/) (2024)
- [Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation](https://mlsanity.com/paper/2408.11812/) (2024)
- [Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space Alignment](https://mlsanity.com/paper/2406.01968/) (2024)
- [Stanford Doggo: An Open-Source, Quasi-Direct-Drive Quadruped](https://mlsanity.com/paper/1905.04254/) (2019)
- [Learning Ball-balancing Robot Through Deep Reinforcement Learning](https://mlsanity.com/paper/2208.10142/) (2022)
- [Design and Control of a Ballbot Drivetrain with High Agility, Minimal Footprint, and High Payload](https://mlsanity.com/paper/2304.02887/) (2023)
- [Overconstrained Locomotion](https://mlsanity.com/paper/2310.09824/) (2023)
- [Reinforcement Learning for Ballbot Navigation in Uneven Terrain](https://mlsanity.com/paper/2505.18417/) (2025)
- [One-DoF Robotic Design of Overconstrained Limbs with Energy-Efficient, Self-Collision-Free Motion](https://mlsanity.com/paper/2509.22002/) (2025)

## Abstract (arXiv)

We introduce asRoBallet, to the best of our knowledge, the first end-to-end reinforcement learning (RL) locomotion policy deployed on a humanoid ballbot hardware platform. Historically, ballbots have served as a canonical benchmark for underactuated and nonholonomic control, which are characterized by a reality gap in complex friction models for wheel-ball-floor interactions. While current literature demonstrates successful handling of 3D balancing with LQR and MPC, transitioning to actual hardware for a humanoid ballbot using RL is currently hindered by critical gaps in contact modeling, actuator latency & jitter, and safe hardware exploration. This study proposes a high-fidelity MuJoCo simulation that explicitly models the discrete roller mechanics of ETH-type omni-wheels, thereby capturing parasitic vibrations and contact discontinuities that have previously been ignored. We also developed a Friction-Aware Reinforcement Learning framework that achieves zero-shot Sim2Real transfer by mastering the coupled rolling, lateral, and torsional friction channels at the wheel-ball and ball-floor interfaces. We designed asRoBallet through subtractive reconfiguration, repurposing key components from an overconstrained quadruped and integrating them into a newly designed structural frame to achieve a robust research platform at low cost. We also developed a generalized iOS ecosystem that transforms consumer electronics into a low-latency interface, enabling a single operator to orchestrate expressive humanoid maneuvers via intuitive natural motion.